Time averaged basal rate optimizer

ABSTRACT

Systems and methods for integrating a continuous glucose sensor, including a receiver, a medicament delivery device, a controller module, and optionally a single point glucose monitor are provided. Integration may be manual, semi-automated and/or fully automated.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application claims the benefit of U.S. ProvisionalApplication No. 61/856,537 filed Jul. 19, 2013. The aforementionedapplication is incorporated by reference herein in its entirety, and ishereby expressly made a part of this specification.

FIELD OF THE INVENTION

An integrated medicament delivery device and continuous glucose sensor,including systems and methods for processing sensor and insulin data,are provided.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high glucose, which may cause an array ofphysiological derangements (for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye) associated with the deteriorationof small blood vessels. A hypoglycemic reaction (low glucose) may beinduced by an inadvertent overdose of insulin, or after a normal dose ofinsulin or glucose-lowering agent accompanied by extraordinary exerciseor insufficient food intake.

Current approaches to open, semi-closed and/or closed loop therapy fordiabetes rely on real-time insulin dosing instructions to replacepre-programmed basal rate infusion in standard insulin pump orcontinuous subcutaneous insulin infusion (CSII) therapy. These systemsgenerally combine real-time continuous glucose monitoring with controlalgorithms to modulate insulin infusion so as to maintain the patients'blood glucose within a specified euglycemic target range.

One of the most significant problems with current open-loop CSII therapyis the difficulty encountered by patients in establishing the correctpattern of basal rates over the course of an entire day. In addition,basal rates that are appropriate to maintain euglycemia on one day witha high level of physical activity may be inadequate on another day witha lower level of physical activity and vice versa. Similarly, basalrates set on one day with a concurrent illness may be inappropriate foranother day with the patient in otherwise good health.

SUMMARY

Time averaging of optimized basal rates is a method for initializing thereal-time basal rate optimization with the best possible starting basalrate profile.

In a first aspect, a method for optimizing a basal rate profile for usewith continuous insulin therapy is provided. The method comprisesproviding a programmed basal rate profile for insulin therapy, whereinthe basal rate profile comprises an insulin delivery schedule thatincludes one or more blocks of time, and wherein each block defines aninsulin delivery rate; periodically or intermittently updating theprogrammed basal rate profile based on a retrospective analysis ofcontinuous glucose sensor data over a predetermined time window; andoptionally adjusting the basal rate profile of the updated programmedbasal rate profile in response to real time continuous glucose sensordata indicative of actual or impending hyperglycemia or hypoglycemia.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the pre-programmed basal rate profile is programmed by a patientor healthcare provider.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the basal rate profile is selected by a user from a list ofpredetermined basal rate profiles.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the method further comprises iteratively repeating the providingand updating, wherein the programmed basal rate profile is an updatedbasal rate profile from a previous iteration. In some embodiments, theprevious iteration is from about one day to one week previous to theiteration.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the basal rate profile consists of a single rate of insulininfusion over 24 hours.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the basal rate profile comprises a plurality of rates associatedwith different time blocks spanning 24 hours.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the retrospective analysis comprises a time-averaging of thecontinuous glucose sensor data.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the periodically or intermittently updating the programmed basalrate profile is further based on a retrospective analysis of insulindata over a predetermined time window. In some embodiments, theretrospective analysis comprises a time-averaging of the insulin data.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the predetermined time window is about 3 to 7 days.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the periodically or intermittently updating is performed once aday.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the periodically or intermittently updating is triggered by anevent.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the periodically or intermittently updating is triggered basedon a recognized pattern in the data. In some embodiments, the recognizedpattern comprises a measure of glycemic variability.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, the updated basal rate profile more closely correlates thepatients' daily insulin dosing requirements as compared to theprogrammed basal rate profile.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, optionally adjusting comprises dynamically increasing ordecreasing the basal rate of the updated programmed basal rate profilein real time in response to real time continuous glucose sensor dataindicating actual or impending hyperglycemia or hypoglycemia

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, periodically or intermittently updating the basal rate profilecomprises providing upper or lower limits insulin delivery.

In a generally applicable embodiment (i.e. independently combinable withany of the aspects or embodiments identified herein) of the firstaspect, optionally adjusting comprises controlling insulin deliverywithin the upper and lower limits.

In a second aspect, an integrated system for monitoring a glucoseconcentration in a host and for delivering insulin to a host, the systemis provided. The system comprises a continuous glucose sensor, whereinthe continuous glucose sensor is configured to substantiallycontinuously measure a glucose concentration in a host, and to providecontinuous sensor data associated with the glucose concentration in thehost; an insulin delivery device configured to deliver insulin to thehost, wherein the insulin delivery device is operably connected to thecontinuous glucose sensor; and a processor module configured to performany one of the embodiments of the first aspect.

Any of the features of an embodiment of the first or second aspects isapplicable to all aspects and embodiments identified herein. Moreover,any of the features of an embodiment of the first or second aspects isindependently combinable, partly or wholly with other embodimentsdescribed herein in any way, e.g., one, two, or three or moreembodiments may be combinable in whole or in part. Further, any of thefeatures of an embodiment of the first or second aspects may be madeoptional to other aspects or embodiments. Any aspect or embodiment of amethod can be performed by a system or apparatus of another aspect orembodiment, and any aspect or embodiment of a system can be configuredto perform a method of another aspect or embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an integrated system of the preferredembodiments, including a continuous glucose sensor and a medicamentdelivery device.

FIG. 2 is a flow chart that illustrates optimization of a basal rateprofile in one embodiment.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the disclosed invention, anumber of terms are defined below.

The term “continuous glucose sensor,” as used herein is a broad term,and is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to a device thatcontinuously or continually measures the glucose concentration of abodily fluid (e.g., blood, plasma, interstitial fluid and the like), forexample, at time intervals ranging from fractions of a second up to, forexample, 1, 2, or 5 minutes, or longer. It should be understood thatcontinual or continuous glucose sensors can continually measure glucoseconcentration without requiring user initiation and/or interaction foreach measurement, such as described with reference to U.S. Pat. No.6,001,067, for example.

The phrase “continuous glucose sensing,” as used herein is a broad term,and is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to the period inwhich monitoring of the glucose concentration of a host's bodily fluid(e.g., blood, serum, plasma, extracellular fluid, etc.) is continuouslyor continually performed, for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.In one exemplary embodiment, the glucose concentration of a host'sextracellular fluid is measured every 1, 2, 5, 10, 20, 30, 40, 50 or60-seconds.

The term “substantially” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to being largely but notnecessarily wholly that which is specified, which may include an amountgreater than 50 percent, an amount greater than 60 percent, an amountgreater than 70 percent, an amount greater than 80 percent, an amountgreater than 90 percent or more.

The terms “processor” and “processor module,” as used herein are a broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to acomputer system, state machine, processor, or the like designed toperform arithmetic or logic operations using logic circuitry thatresponds to and processes the basic instructions that drive a computer.In some embodiments, the terms can include ROM and/or RAM associatedtherewith.

The term “basal,” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to the minimum required rate or other valuefor something to function. For example, in the case of insulin therapy,the term “basal rate” can refer to a regular (e.g., in accordance withfixed order or procedure, such as regularly scheduled for/at a fixedtime), periodic or continuous delivery of low levels of insulin, such asbut not limited to throughout a 24-hour period.

The term “basal rate profile,” as used herein is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to an insulin delivery schedulethat includes one or more blocks of time (e.g., time blocks), whereineach block defines an insulin delivery rate.

Exemplary embodiments disclosed herein relate to the use of a glucosesensor that measures a concentration of glucose or a substanceindicative of the concentration or presence of the analyte. In someembodiments, the glucose sensor is a continuous device, for example asubcutaneous, transdermal, transcutaneous, and/or intravascular (e.g.,intravenous) device. In some embodiments, the device can analyze aplurality of intermittent blood samples. The glucose sensor can use anymethod of glucose-measurement, including enzymatic, chemical, physical,electrochemical, optical, optochemical, fluorescence-based,spectrophotometric, spectroscopic (e.g., optical absorptionspectroscopy, Raman spectroscopy, etc.), polarimetric, calorimetric,iontophoretic, radiometric, and the like.

The glucose sensor can use any known detection method, includinginvasive, minimally invasive, and non-invasive sensing techniques, toprovide a data stream indicative of the concentration of the analyte ina host. The data stream is typically a raw data signal that is used toprovide a useful value of the analyte to a user, such as a patient orhealth care professional (e.g., doctor), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor, the systems and methods of embodiments can be applied to anymeasurable analyte. In some embodiments, the analyte sensor is a glucosesensor capable of measuring the concentration of glucose in a host. Someexemplary embodiments described below utilize an implantable glucosesensor. However, it should be understood that the devices and methodsdescribed herein can be applied to any device capable of detecting aconcentration of analyte and providing an output signal that representsthe concentration of the analyte.

In some embodiments, the analyte sensor is an implantable glucosesensor, such as described with reference to U.S. Pat. No. 6,001,067 andU.S. Patent Publication No. US-2011-0027127-A1. In some embodiments, theanalyte sensor is a transcutaneous glucose sensor, such as describedwith reference to U.S. Patent Publication No. US-2006-0020187-A1. In yetother embodiments, the analyte sensor is a dual electrode analytesensor, such as described with reference to U.S. Patent Publication No.US-2009-0137887-A1. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Patent Publication No. US-2007-0027385-A1. The patentsand publications are incorporated herein by reference in their entirety.

In order to improve diabetes management, therapy in an open, semi-closedand/or closed loop therapy can be provided that performs a periodicoptimization of the pre-programmed basal rate profile alone based oninput from a continuous glucose monitor. The optimized basal rateprofile can increase the effectiveness of a real-time basal rateadjustment because the basal rate profile is optimized to correlate tothe patients' unique and changing daily insulin requirements. Real timebasal rate optimization within specified upper and lower limits canprovide patients with improved glycemic control with a minimum risk ofinsulin over administration. In some embodiments, the basal rate profileoptimizer provides upper and lower limits for the real-time basal rateadjustment, which may be defined as multiples of the pre-existing orpre-programmed basal rate, e.g. fractional values of less than 1 andgreater than or equal to 0 to reduce insulin infusion in response tomeasured or predicted hypoglycemia and fractional values greater than 1and less than or equal to 2 to increase insulin infusion in response tomeasured or predicted hyperglycemia.

For illustrative purposes, reference will now be made to FIG. 1, whichis an exemplary environment in which some embodiments described hereinmay be implemented. Here, an analyte monitoring system 100 includes acontinuous analyte sensor system 8. Continuous analyte sensor system 8includes a sensor electronics module 12 and a continuous analyte sensor10. The system 100 can also include other devices and/or sensors, suchas a medicament delivery pump 2 and a reference analyte meter 4, asillustrated in FIG. 1. The continuous analyte sensor 10 may bephysically connected to sensor electronics module 12 and may be integralwith (e.g., non-releasably attached to) or releasably attachable to thecontinuous analyte sensor 10. Alternatively, the continuous analytesensor 10 may be physically separate to sensor electronics module 12,but electronically coupled via inductive coupling or the like. Further,the sensor electronics module 12, medicament delivery pump 2, and/oranalyte reference meter 4 may communicate with one or more additionaldevices, such as any or all of display devices 14, 16, 18 and 20.

The system 100 of FIG. 1 also includes a cloud-based processor 22configured to analyze analyte data, medicament delivery data and/orother patient related data provided over network 24 directly orindirectly from one or more of sensor system 8, medicament delivery pump2, reference analyte meter 4, and display devices 14, 16, 18, 20. Basedon the received data, the processor 22 can further process the data,generate reports providing statistic based on the processed data,trigger notifications to electronic devices associated with the host orcaretaker of the host, or provide processed information to any of theother devices of FIG. 1. In some exemplary implementations, thecloud-based processor 22 comprises one or more servers. If thecloud-based processor 22 comprises multiple servers, the servers can beeither geographically local or separate from one another. The network 24can include any wired and wireless communication medium to transmitdata, including WiFi networks, cellular networks, the Internet and anycombinations thereof.

It should be understood that although the example implementationdescribed with respect to FIG. 1 refers to analyte data being receivedby processor 22, other types of data processed and raw data may bereceived as well.

In some exemplary implementations, the sensor electronics module 12 mayinclude electronic circuitry associated with measuring and processingdata generated by the continuous analyte sensor 10. This generatedcontinuous analyte sensor data may also include algorithms, which can beused to process and calibrate the continuous analyte sensor data,although these algorithms may be provided in other ways as well. Thesensor electronics module 12 may include hardware, firmware, software,or a combination thereof to provide measurement of levels of the analytevia a continuous analyte sensor, such as a continuous glucose sensor.

The sensor electronics module 12 may, as noted, couple (e.g., wirelesslyand the like) with one or more devices, such as any or all of displaydevices 14, 16, 18, and 20. The display devices 14, 16, 18, and/or 20may be configured for processing and presenting information, such sensorinformation transmitted by the sensor electronics module 12 for displayat the display device. The display devices 14, 16, 18, and 20 can alsotrigger alarms based on the analyte sensor data.

In FIG. 1, display device 14 is a key fob-like display device, displaydevice 16 is a hand-held application-specific computing device 16 (e.g.the DexCom G4® Platinum receiver commercially available from DexCom,Inc.), display device 18 is a general purpose smart phone or tabletcomputing device 20 (e.g. an Apple® iPhone®, iPad®, or iPod Touch®commercially available from Apple, Inc.), and display device 20 is acomputer workstation 20. In some exemplary implementations, therelatively small, key fob-like display device 14 may be a computingdevice embodied in a wrist watch, a belt, a necklace, a pendent, a pieceof jewelry, an adhesive patch, a pager, a key fob, a plastic card (e.g.,credit card), an identification (ID) card, and/or the like. This smalldisplay device 14 may include a relatively small display (e.g., smallerthan the display device 18) and may be configured to display a limitedset of displayable sensor information, such as a numerical value 26 andan arrow 28. In contrast, display devices 16, 18 and 20 can be largerdisplay devices that can be capable of displaying a larger set ofdisplayable information, such as a trend graph 30 depicted on thehand-held receiver 16 in addition to other information such as anumerical value and arrow.

It is understood that any other user equipment (e.g. computing devices)configured to at least present information (e.g., a medicament deliveryinformation, discrete self-monitoring analyte readings, heart ratemonitor, caloric intake monitor, and the like) can be used in additionor instead of those discussed with reference to FIG. 1.

In some exemplary implementations of FIG. 1, the continuous analytesensor 10 comprises a sensor for detecting and/or measuring analytes,and the continuous analyte sensor 10 may be configured to continuouslydetect and/or measure analytes as a non-invasive device, a subcutaneousdevice, a transdermal device, and/or an intravascular device. In someexemplary implementations, the continuous analyte sensor 10 may analyzea plurality of intermittent blood samples, although other analytes maybe used as well.

In some exemplary implementations of FIG. 1, the continuous analytesensor 10 may comprise a glucose sensor configured to measure glucose inthe blood using one or more measurement techniques, such as enzymatic,chemical, physical, electrochemical, spectrophotometric, polarimetric,calorimetric, iontophoretic, radiometric, immunochemical, and the like.In implementations in which the continuous analyte sensor 10 includes aglucose sensor, the glucose sensor may be comprise any device capable ofmeasuring the concentration of glucose and may use a variety oftechniques to measure glucose including invasive, minimally invasive,and non-invasive sensing techniques (e.g., fluorescent monitoring), toprovide a data, such as a data stream, indicative of the concentrationof glucose in a host. The data stream may be raw data signal, which isconverted into a calibrated and/or filtered data stream used to providea value of glucose to a host, such as a user, a patient, or a caretaker(e.g., a parent, a relative, a guardian, a teacher, a doctor, a nurse,or any other individual that has an interest in the wellbeing of thehost). Moreover, the continuous analyte sensor 10 may be implanted as atleast one of the following types of sensors: an implantable glucosesensor, a transcutaneous glucose sensor, implanted in a host vessel orextracorporeally, a subcutaneous sensor, a refillable subcutaneoussensor, an intravascular sensor.

In some implementations of FIG. 1, the continuous analyte sensor system8 includes a DexCom G4® Platinum glucose sensor and transmittercommercially available from DexCom, Inc., for continuously monitoring ahost's glucose levels.

FIG. 2 is a flow chart that illustrates optimization of a basal rateprofile in accordance with some embodiments. Here, a processor module isconfigured to periodically optimize a basal rate profile using atime-averaged basal rate optimization performed over the previous about3 to 7 days, which adjusts, or augments, the pre-programmed basal rateprofile based thereon. The processor module can be embodied in any ofthe electronic devices described with reference to FIG. 1, such as thesensor system 8, medicament pump 2, reference meter 4, display device14-20 and cloud-based processor 22. Further, the processor module neednot be physically localized to a single electronic device, but can beseparated between multiple devices. That is, the processor module can bephysically divided between two more computing devices, such as sensorelectronics 12 and medicament pump 2, or display device 16 andcloud-based processor 22.

In some embodiments, the retrospective time-averaged basal rateoptimization utilizes sensor data from the continuous glucose sensor,for example, including periods of time spanning skipped meals perexisting basal rate adjustment recommendations (see, e.g., Zisser H C,Bevier W C, Jovanovic L “Restoring euglycemia in the basal state usingcontinuous glucose monitoring in subjects with type 1 diabetes mellitus”Diabetes Technol. Ther. 2007 December; 9(6):509-15) or, alternatively,from interpretation of meal data along with insulin data and thenutritional information for the meal. The output of the time-averagedbasal rate optimization can be updated daily, weekly, or the like, toadjust the pre-programmed basal rate profile. In some implementations, adynamic real-time basal rate optimizer operates to adjust the basal ratein real time within safety bounds determined by the optimized basal rateprofile.

Basal rate profile optimization 200 is described in FIG. 2. At block 202of FIG. 2, the processor module provides a programmed basal rateprofile. For example, the pre-programmed basal rate profile can beprogrammed by a patient based on a consultation with a health careprovider or by the patient alone. The basal rate may be selected from alist of predefined profiles provided by the manufacturer and/or manuallydefined by a user. In a feedback loop of the flowchart, the programmedbasal rate profile at block 202 is an optimized basal rate profile froma previous update (at block 204), for example, from a previous day orweek. The basal rate profile may consist of a single rate of insulininfusion over 24 hours or a plurality of rates associated with differenttime windows spanning a full 24 hours, as would be appreciated by one ofordinary skill in the art.

At block 204, the processor module updates the programmed basal rateprofile of block 202, periodically or intermittently, based on aretrospective analysis of the continuous glucose sensor data (e.g.,measured using sensor system 8 of FIG. 1) and optionally insulin data(e.g., generated by medicament delivery pump 2 of FIG. 1), if available,over a predetermined time window (e.g., about 3 to 7 days) for aparticular patient. The updated basal rate profile may include a singlebasal rate profile for a particular patient, a profile defined by upperand lower limits (e.g., a range) for the maximum and minimum basal ratesfor a given patient and/or a combination of both.

The periodic or intermittent update can be performed once a day,triggered by an event or triggered based on a recognized pattern in thedata, such as glycemic variability. A triggering event may be a failureof the real-time basal rate optimization to prevent a severehypoglycemic episode (e.g., less than 55 mg/dL), a severe hyperglycemicepisode (e.g., greater than 250 mg/dL) and/or a predefined pattern ofsevere hypoglycemic episodes or a severe hyperglycemic episode over thepast window of time (e.g., 3-7 days). Pattern recognition algorithmsthat identify a predefined combination of frequency and severity of anevent, such as described in such as described in co-pending patentapplication Ser. No. 13/566,678, filed 3 Aug. 2013 and Ser. No.13/790,281, filed 8 Mar. 2013, may be useful as a triggering event.Additionally or alternatively, a predetermined pattern or repetition ofhypoglycemia or hyperglycemia at certain times of day might trigger anupdate to the basal rate profile, including, for example, a decrease orincrease in the pre-programmed basal rates at certain time blocksassociated with the certain time of an identified pattern of hypo- orhyper-glycemia. Additional measures of glycemic variability, which maybe used to trigger the update, have been described by Marling C R,Struble N W, Bunescu R C, Shubrook J H and Schwartz F L “A consensusperceived glycemic variability metric” J Diabetes Sci Technol 2013;7(4): 871-879.

The time-averaged basal rate optimization utilizes data over a movingwindow of previous days to determine an optimized basal rate profilewhich then provides input for and/or optionally safety bounds for thereal-time basal rate adjustment and/or other closed loop controlalgorithm (at block 206). While not wishing to be bound by theory, theuse of time averaging helps to reduce the effect of single day anomalieson the setting of the pre-programmed basal rates. The time-averagedbasal rate optimization can be based on: continuous glucose sensor data,insulin delivery data, content of meals and/or physical activity duringthe period over which the time-averaging is being performed. Thepredetermined time window can be one day, 3 to 7 days, or longer. Insome embodiments, the time window can be limited to reduce the effect ofactual long-term changes in behavior or physical activity levels, forexample, no more than 7, 14, 21 or 30 days. By periodically updating thebasal rate profile based on a retrospective analysis of an immediatelypreceding time window of data, the updated programmed basal rate profilematches (e.g., more closely correlates) the patients' daily insulindosing requirements as compared to the previous programmed basal rateprofile. The matching or correlation of the basal rate profile with thepatients' daily insulin dosing requirement can be quantified a measureof glycemic variability (e.g., time in/out target/euglycemia),especially in the absence of meals or other inputs that affect glycemiclevels. The time averaged basal rate optimization can be performedlocally on analyte sensor system 8, one of the display devices 14-20,medicament delivery pump 2, cloud-based processor 22, or the like.

In some embodiments detects sub-optimal basal rate profiles, andsuggests improvements (to the patient, provider, or both). For example,prior to the processor module updating the programmed basal rateprofile, the processor module may provide output indicative of asuboptimal basal rate profile, which may be detected based on apredetermined difference between the predetermined basal rate profileand the updated basal rate profile. The processor module may then outputa message to a patient (e.g., via a prompt on a user interface) or to acare provider (e.g., via a message delivered wirelessly or via theinternet) recommending the updated basal rate profile. The patient orcare provider may then select or adjust the updated programmable basalrate profile, after which the processor module implements the updatedbasal rate profile on the medicament pump 2.

While personalized updating of the basal rate profile as described atblock 204 is advantageous for use in optimizing stand-alone insulin pumptherapy, the updated basal rate profile may also be used in closed loopor semi-closed loop system to improve the efficacy of closed loopalgorithms. Namely, further improvements in semi-closed or closed loopalgorithms may be achieved, over systems that use real time basal rateadjustments without utilizing the personalized basal rate profilesupdates, by minimizing the required adjustment by the real-time basalrate adjustment due to the already personalized basal rate profile,which is described in more detail at block 206.

At block 206, the processor module dynamically adjusts (or augments), inreal time, (e.g., increases or decreases) the basal rate of the updatedprogrammed basal rate profile of block 204 in response to real timesensor data indicating actual or impending hyperglycemia orhypoglycemia. The indication of actual or impending hypoglycemia may bedetermined by comparing threshold criteria with estimated real time orpredicted glucose concentration values, for example. The real-timeadjustment of block 206 may be performed more often than the updating ofthe programmed basal rate profile of block 204 and generally utilizes ashorter time window of data and/or prediction of future glucose valuesand/or insulin-on-board information, for example, as compared to thetime window of data used for the retrospective analysis of block 204.While not wishing to be bound by theory, the clinical effectiveness of areal-time basal rate dynamic adjust (or other closed loop controlalgorithm) of block 206 in providing incremental increases or decreasesin basal insulin infusion in response to the real time sensor data isenhanced by utilizing an optimized programmed basal rate profile ofblock 204 as a starting point for insulin delivery.

Additionally or alternatively, retrospective analysis of continuousglucose monitoring data as part of the real-time basal rate adjustmentand/or other closed loop control algorithm (at block 204) can provideupper and lower limits for the maximum and minimum basal rates for agiven patient. For example, the upper and lower limits, defined by upperand lower basal rate profiles determined at block 204, may be applied tobasal rates and/or other closed loop control algorithm. While notwishing to be bound by theory, it is believed that if the pre-programmedbasal rate is not well correlated with the patient's daily insulindosing requirements (as provided at block 204), then the safetyconstraints on the real-time basal rate adjustment or other closed loopcontrol algorithm (at block 206) may limit the method and system fromachieving good outcomes.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the Figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure (such as the blocks of FIG. 2)may be implemented or performed with a general purpose processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array signal (FPGA) or otherprogrammable logic device (PLD), discrete gate or transistor logic,discrete hardware components or any combination thereof designed toperform the functions described herein. A general purpose processor maybe a microprocessor, but in the alternative, the processor may be anycommercially available processor, controller, microcontroller or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a combination of a DSP and a microprocessor, aplurality of microprocessors, one or more microprocessors in conjunctionwith a DSP core, or any other such configuration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Also, any connectionis properly termed a computer-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Thus, in some aspects computer readable medium may comprisenon-transitory computer readable medium (e.g., tangible media). Inaddition, in some aspects computer readable medium may comprisetransitory computer readable medium (e.g., a signal). Combinations ofthe above should also be included within the scope of computer-readablemedia.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit, and each intervening value between the upper and lowerlimit of the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method for optimizing a basal rate profile foruse with continuous insulin therapy, comprising: providing a programmedbasal rate profile for insulin therapy, wherein the basal rate profilecomprises an insulin delivery schedule that includes one or more blocksof time, and wherein each block of time defines an insulin deliveryrate; periodically or intermittently updating the programmed basal rateprofile based on a retrospective analysis of continuous glucose sensordata over a predetermined time window; and adjusting the basal rateprofile of the updated programmed basal rate profile in response to realtime continuous glucose sensor data indicative of actual or impendinghyperglycemia or hypoglycemia, wherein adjusting comprises dynamicallyincreasing or decreasing the basal rate of the updated programmed basalrate profile in real time in response to real time continuous glucosesensor data indicating actual hyperglycemia, impending hyperglycemia,actual hypoglycemia, or impending hypoglycemia.
 2. The method of claim1, wherein the programmed basal rate profile is pre-programmed by apatient or healthcare provider.
 3. The method of claim 1, wherein thebasal rate profile is selected by a user from a list of predeterminedbasal rate profiles.
 4. The method of claim 1, further comprisingiteratively repeating the providing and periodically or intermittentlyupdating, wherein the programmed basal rate profile is an updated basalrate profile from a previous iteration.
 5. The method of claim 4,wherein the previous iteration is from about one day to one weekprevious to the iteration.
 6. The method of claim 1, wherein the basalrate profile consists of a single rate of insulin infusion over 24hours.
 7. The method of claim 1, wherein the basal rate profilecomprises a plurality of rates associated with different time blocksspanning 24 hours.
 8. The method of claim 1, wherein the retrospectiveanalysis comprises a time-averaging of the continuous glucose sensordata.
 9. The method of claim 1, wherein the periodically orintermittently updating the programmed basal rate profile is furtherbased on a retrospective analysis of insulin data over a predeterminedtime window.
 10. The method of claim 9, wherein the retrospectiveanalysis comprises a time-averaging of the insulin data.
 11. The methodof claim 1, wherein the predetermined time window is about 3 days toabout 7 days.
 12. The method of claim 1, wherein the periodically orintermittently updating is performed once a day.
 13. The method of claim1, wherein the periodically or intermittently updating is triggered byan event.
 14. The method of claim 1, wherein the periodically orintermittently updating is triggered based on a recognized pattern inthe data.
 15. The method of claim 14, wherein the recognized patterncomprises a measure of glycemic variability.
 16. The method of claim 1,wherein the updated basal rate profile more closely matches thepatients' daily insulin dosing requirements as compared to theprogrammed basal rate profile, wherein the updated basal rate profilemore closely matching the patients' daily insulin dosing requirements isquantified by a measure of glycemic variability.
 17. The method of claim1, wherein periodically or intermittently updating the basal rateprofile comprises providing upper or lower limits for insulin delivery.18. The method of claim 17, wherein adjusting comprises controllinginsulin delivery within the upper and lower limits.
 19. An integratedsystem for monitoring a glucose concentration in a host and fordelivering insulin to a host, comprising: a continuous glucose sensor,wherein the continuous glucose sensor is configured to substantiallycontinuously measure a glucose concentration in a host, and to providecontinuous sensor data associated with the glucose concentration in thehost; an insulin delivery device configured to deliver insulin to thehost, wherein the insulin delivery device is operably connected to thecontinuous glucose sensor; and a processor module configured to providea programmed basal rate profile for insulin therapy, wherein the basalrate profile comprises an insulin delivery schedule that includes one ormore blocks of time, and wherein each block defines an insulin deliveryrate, periodically or intermittently update the programmed basal rateprofile based on a retrospective analysis of continuous glucose sensordata over a predetermined time window, and adjust the basal rate profileof the updated programmed basal rate profile in response to real timecontinuous glucose sensor data indicative of actual or impendinghyperglycemia or hypoglycemia, wherein the adjustment comprisesdynamically increasing or decreasing the basal rate of the updatedprogrammed basal rate profile in real time in response to real timecontinuous glucose sensor data indicating actual hyperglycemia,impending hyperglycemia, actual hypoglycemia, or impending hypoglycemia.20. The system of claim 19, wherein the programmed basal rate profile ispre-programmed by a patient or healthcare provider.
 21. The system ofclaim 19, wherein the basal rate profile is selected by a user from alist of predetermined basal rate profiles.
 22. The system of claim 19,wherein the processor is further configured to iteratively repeat theproviding and the periodically or intermittently updating, wherein theprogrammed basal rate profile is an updated basal rate profile from aprevious iteration.
 23. The system of claim 22, wherein the previousiteration is from about one day to one week previous to the iteration.24. The system of claim 19, wherein the basal rate profile consists of asingle rate of insulin infusion over 24 hours.
 25. The system of claim19, wherein the basal rate profile comprises a plurality of ratesassociated with different time blocks spanning 24 hours.
 26. The systemof claim 19, wherein the retrospective analysis comprises atime-averaging of the continuous glucose sensor data.
 27. The system ofclaim 19, wherein the periodically or intermittently updating theprogrammed basal rate profile is further based on a retrospectiveanalysis of insulin data over a predetermined time window.
 28. Thesystem of claim 27, wherein the retrospective analysis comprises atime-averaging of the insulin data.
 29. The system of claim 19, whereinthe predetermined time window is about 3 days to about 7 days.
 30. Thesystem of claim 19, wherein the periodically or intermittently updatingis performed once a day.
 31. The system of claim 19, wherein theperiodically or intermittently updating is triggered by an event. 32.The system of claim 19, wherein the periodically or intermittentlyupdating is triggered based on a recognized pattern in the data.
 33. Thesystem of claim 32, wherein the recognized pattern comprises a measureof glycemic variability.
 34. The system of claim 19, wherein the updatedbasal rate profile more closely matches the patients' daily insulindosing requirements as compared to the programmed basal rate profile,wherein the updated basal rate profile more closely matching thepatients' daily insulin dosing requirements is quantified by a measureof glycemic variability.
 35. The system of claim 19, whereinperiodically or intermittently updating the basal rate profile comprisesproviding upper or lower limits for insulin delivery.
 36. The system ofclaim 35, wherein optionally adjusting comprises controlling insulindelivery within the upper and lower limits.